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Intensity diffraction tomography with controllable LED illumination
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Computational Microscopy Using LED Array for 3D Phase Imaging - Prof. Lei Tian
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- 1 Computational microscopy using an LED array
- 2 LED array microscope for multimodal computational imaging
- 3 Intensity diffraction tomography with controllable LED illumination
- 4 Linear single-scattering approximation model Sample
- 5 Single scattering information is directly visible in the Fourier space
- 6 3D reconstruction by slice-wise deconvolution
- 7 Scan-free 3D phase reconstruction on cell cluster
- 8 Expanding IDT's imaging limits
- 9 Multiplexed Intensity Diffraction Tomography (MIDT) for high volume-rate IDT
- 10 Multi-scale phase tomography
- 11 Annual IDT (alDT) further extends imaging limits
- 12 Real-time volumetric phase imaging
- 13 "Open" computational 3D phase microscopy
- 14 Single-scattering model limits IDT's reconstructio
- 15 Multiple-scattering model to improve accuracy
- 16 Nonlinear SSNP multiple-scattering model
- 17 Reconstruction by optimization
- 18 Reconstruction on multiple-scattering sample
- 19 Reconstruction on dynamic sample
- 20 Deep learning training leverages single-scattering! inversion and learns multiple-scattering physics
- 21 Deep learning training leverages single-scattering inversion and learns multiple-scattering physics
- 22 Network improves weak scattering recovery
- 23 Network generalizes to different optical setups
- 24 Network generalizes to dynamic multiple-scattering sami
- 25 Acknowledgements
- 26 Model and learning strategies for Computational 3D phase microscopy
- 27 Lightweight 2D U-Net for efficient 3D Recovery
- 28 Multiple-scattering model-based reconstruction is still limited
- 29 Compute SSNP forward model stepwise
- 30 Model-based data normalization for generalization across different contrasts
- 31 Network prediction validation
- 32 Multiple-scattering physical simulator for generating training data Neural network trained entirely on simulated data generated using multiple scattering models
- 33 Trained deep learning model generalizes to biological samples
